Citations

Abstract Studies have identified genetic and epidemiologic factors associated with mammographic density (MD) phenotypes. However, MD-associated genetic variants only account for a small proportion of the total estimated heritability. Interrogating interactions between genetic and epidemiologic factors could potentially identify additional MD-associated loci, expand our understanding of the genetic basis of MD phenotypes, and clarify how epidemiologic factors modulate relationships between genetic variants and MD. We conducted six separate genome-wide, gene–environment (GxE) interaction analyses, applying 2 degrees of freedom (df) and 1df interaction tests, for each of three MD phenotypes (percent density, dense area (DA), and nondense area (NDA)). The six epidemiologic factors considered were height, ever parous, parity, ever menopausal hormone therapy, ever breastfeeding, and months of breastfeeding. We included European ancestry participants from multiple studies within the Markers of Density consortium and the Breast Cancer Association Consortium (n = 4895-16 218 depending on specific analyses). We identified 11 loci with genome-wide significant (P < 5 × 10−8) interaction tests including two novel common genetic signals interacting with parity (8p21.2) and ever breastfeeding (19p13.2) for NDA. Our results suggest that epidemiologic risk factors might influence relationships between common genetic variants and MD phenotypes at particular genomic loci.

Objectives/Goals: Manual skin assessment in chronic graft-versus-host disease (cGVHD) can be time consuming and inconsistent (>20% affected area) even for experts. Building on previous work we explore methods to use unmarked photos to train artificial intelligence (AI) models, aiming to improve performance by expanding and diversifying the training data without additional burden on experts. Methods/Study Population: Common to many medical imaging projects, we have a small number of expert-marked patient photos (N = 36, n = 360), and many unmarked photos (N = 337, n = 25,842). Dark skin (Fitzpatrick type 4+) is underrepresented in both sets; 11% of patients in the marked set and 9% in the unmarked set. In addition, a set of 20 expert-marked photos from 20 patients were withheld from training to assess model performance, with 20% dark skin type. Our gold standard markings were manual contours around affected skin by a trained expert. Three AI training methods were tested. Our established baseline uses only the small number of marked photos (supervised method). The semi-supervised method uses a mix of marked and unmarked photos with human feedback. The self-supervised method uses only unmarked photos without any human feedback. Results/Anticipated Results: We evaluated performance by comparing predicted skin areas with expert markings. The error was given by the absolute difference between the percentage areas marked by the AI model and expert, where lower is better. Across all test patients, the median error was 19% (interquartile range 6 – 34) for the supervised method and 10% (5 – 23) for the semi-supervised method, which incorporated unmarked photos from 83 patients. On dark skin types, the median error was 36% (18 – 62) for supervised and 28% (14 – 52) for semi-supervised, compared to a median error on light skin of 18% (5 – 26) for supervised and 7% (4 – 17) for semi-supervised. Self-supervised, using all 337 unmarked patients, is expected to further improve performance and consistency due to increased data diversity. Full results will be presented at the meeting. Discussion/Significance of Impact: By automating skin assessment for cGVHD, AI could improve accuracy and consistency compared to manual methods. If translated to clinical use, this would ease clinical burden and scale to large patient cohorts. Future work will focus on ensuring equitable performance across all skin types, providing fair and accurate assessments for every patient.

Abstract Allogeneic hematopoietic cell transplantation (allo-HCT) is potentially curative for older adults with hematologic malignancies. Concerns on nonrelapse mortality (NRM) in older adults limit allo-HCT utilization. We executed a prospective, observational study BMT-CTN 1704 (Blood and Marrow Transplant Clinical Trials Network) enrolling allo-HCT recipients aged ≥60 years from 49 centers in the United States. We analyzed associations between 13 measurements of older adult health and NRM within 1 year to construct a comprehensive health assessment risk model (primary-CHARM) using multivariate Fine-Gray model and grouped penalized variable selection. Two machine learning (ML) models (Cox and pseudo-value boosting) were also explored. Models’ performances were compared using area under the curve (AUC), with bootstrap and cross-validation sampling to correct for optimism, decision curve analysis (DCA), calibration, and Brier scores. Among 1105 patients with median age of 67 (range, 60-82) years who received allo-HCT, NRM was 14.4% and overall survival (OS) 71.7% at 1 year. Factors statistically selected for inclusion in primary-CHARM were higher comorbidity burden, lower albumin, higher C-reactive protein, older age, higher weight-loss percentage, lower patient-reported performance score, and cognitive impairment. Primary-CHARM scores were independently associated with higher NRM (hazard ratio [HR], 2.72; P < .0001) and worse OS (HR, 2.09; P < .0001). Bootstrap bias–corrected AUC for primary-CHARM was 0.591. Comparing primary-CHARM with HCT-comorbidity index and 2 ML-CHARM models, calibration, Brier score, and DCA analysis favored primary-CHARM. Primary-CHARM, with mostly simple and readily available parameters, risk stratifies older adults for allo-HCT. Adopting primary-CHARM in practice may promote broader use of HCT by quantifying risk and enhance the design of strategies to improve outcomes. This trial was registered at www.ClinicalTrials.gov as #NCT03992352.

The NCCN Guidelines for Myelodysplastic Syndromes (MDS) provide recommendations for the evaluation, diagnosis, and comprehensive care of patients with MDS based on a review of recent clinical evidence that has led to important advances in treatment or has yielded new information on biologic factors that may have prognostic significance in MDS. The multidisciplinary panel of MDS experts is convened at least on an annual basis. During the annual meeting, the panel evaluates new and emerging data to inform their recommendations. These NCCN Guidelines Insights review the recent updates, including treatment recommendations both for lower-risk and higher-risk MDS, preference stratification of therapeutic agents, and emerging data on novel therapeutics.

The NCCN Clinical Practice Guidelines in Oncology (NCCN Guidelines) for Pediatric Central Nervous System Cancers provide multidisciplinary diagnostic workup, staging, and treatment recommendations for diffuse high-grade gliomas and medulloblastomas in children and adolescents. This article summarizes the studies and panel discussion that serve as the rationale for comprehensive care recommendations included in the NCCN Guidelines for Pediatric Central Nervous System Cancers.

Abstract Overall survival (OS) and quality of life (QoL) are clinically relevant outcomes/end points during examination of therapies. However, with median OS approaching 20 years for patients with follicular lymphoma (FL), it is often not feasible to use OS as a primary end point in clinical studies. Although validated tools exist for assessing QoL in patients with lymphoma, QoL data are rarely the sole basis for drug approvals in FL. Therefore, other survival end points, surrogates, and intermediate clinical end points have all been used to measure outcomes in FL trials. In this review, we discuss the strengths and limitations of these commonly used traditional end points in FL trials and examine the current gaps, including delayed availability of results and suboptimal sensitivity in distinguishing difference in therapeutic effects. To help address the identified gaps, well-validated surrogates, such as complete remission 30 months after starting frontline immunochemotherapy, may be used as the primary or co-primary end point in confirmatory randomized trials. Emerging intermediate end points like minimal residual disease may be useful in early phase trials and in guiding accelerated approvals after appropriate validation. As patient preference plays a crucial role in treatment decisions in FL, it is critical to include QoL as an important secondary trial end point and to measure nonmedical burdens, including time and financial toxicities. End points that are clinically relevant, timely, and patient-centered may identify new drugs that help patients with FL live longer and better lives.

Abstract Background: Ovarian high-grade serous carcinomas (HGSC) comprise four distinct molecular subtypes based on mRNA expression patterns, with differential survival. Understanding risk factor associations is important to elucidate the etiology of HGSC. We investigated associations between different epidemiologic risk factors and HGSC molecular subtypes. Methods: We pooled data from 11 case–control studies with epidemiologic and tumor gene expression data from custom NanoString CodeSets developed through a collaboration within the Ovarian Tumor Tissue Analysis consortium. The PrOTYPE-validated NanoString-based 55-gene classifier was used to assign HGSC gene expression subtypes. We examined associations between epidemiologic factors and HGSC subtypes in 2,070 cases and 16,633 controls using multivariable-adjusted polytomous regression models. Results: Among the 2,070 HGSC cases, 556 (27%) were classified as C1.MES, 340 (16%) as C5.PRO, 538 (26%) as C2.IMM, and 636 (31%) as C4.DIF. The key factors, including oral contraceptive use, parity, breastfeeding, and family history of ovarian cancer, were similarly associated with all subtypes. Heterogeneity was observed for several factors. Former smoking [OR = 1.25; 95% confidence interval (CI) = 1.03, 1.51] and genital powder use (OR = 1.42; 95% CI = 1.08, 1.86) were uniquely associated with C2.IMM. History of endometriosis was associated with C5.PRO (OR = 1.46; 95% CI = 0.98, 2.16) and C4.DIF (OR = 1.27; 95% CI = 0.94, 1.71) only. Family history of breast cancer (OR = 1.44; 95% CI = 1.16, 1.78) and current smoking (OR = 1.40; 95% CI = 1.11, 1.76) were associated with C4.DIF only. Conclusions: This study observed heterogeneous associations of epidemiologic and modifiable factors with HGSC molecular subtypes. Impact: The different patterns of associations may provide key information about the etiology of the four subtypes.

Abstract Interpreting cancer clinical trial results often depends on addressing issues of multiplicity. When testing multiple hypotheses, unreliable findings can occur by chance due to the inflation of the type I error rate, the probability of mistakenly rejecting the null hypothesis when the null hypothesis is true. In this setting, researchers may often set the type I error rate (or the alpha level) low to limit false positive findings and the interpretation of a causal relationship where none exists. Conversely, overly conservative type I error control may result in declaring findings, that do not meet multiplicity-adjusted alpha levels, as false when they are actually true, reducing opportunities for new discovery. This presentation focuses on multiplicity adjustment in the context of clinical trials conducted within the NCI’s Community Oncology Research Program (NCORP). Because federally sponsored trials often require long-term participation from patients and represent a substantial investment by taxpayers, striking the right balance between optimizing what is learned from these trials, while avoiding false positive results, should be a priority.

Abstract Examining treatment effects in subgroups of patients defined by demographic, genetic, or clinical characteristics is increasingly of interest given the pursuit of personalized medicine and the importance of representation and equity in treatment decisions. The magnitude or even the direction of the treatment effect may vary across subgroups, and these differential treatment effects could have clinical implications. Subgroup analyses require caution in their interpretation, however, because of the high probability of a false-positive or false-negative conclusion. We outline study design and analysis considerations for responsibly investigating and reporting differential treatment effects across subgroups in oncology trials, with examples from the National Cancer Institute’s National Clinical Trials Network and Community Oncology Research Program. Recommendations include ensuring appropriate representation of patients from subgroups of interest, recognizing power and multiplicity limitations, and treating exploratory subgroup analyses as hypothesis generating rather than practice changing.